Mixed Model Methodology has received considerable practical interest over the last two decades. This is due primarily to the following two features: a) MM are tools of choice for analyzing correlated data in a broad area of situations (block x treatment designs; clusters, longitudinal and spatial patterns); b) MM are also increasingly feasible through more and more efficient algorithms (e.g., EM, Average information) and softwares (e.g SAS Proc Mixed, ASReml, R-lme4, Monolix, Nlme, Winbugs, etc...).
We are now in a new stage involving more sophisticated modelling approaches eg multi-level modelling (population and individual; mean and variance models) and also the use of dynamic systems for functional data.
The object of thist text is to present a synthetic approach of the basic theory underlying linear mixed models. At a time when computers and software are easily available and applicable, the purpose of this text is to provide the reader with the elements of a better understanding and mastering of concepts he will use in practice. This is particularly important as most of the existing literature is directed to specific audiences (software -oriented), topics (eg longitudinal data) or methods (maximum likelihood, bayesian approaches).
This course is intended for a broad audience of graduate students, researchers and scientists who are seeking to learn about the foundations of mixed models starting with the linear ones, modelling options and how to apply them in the field of biology, medicine, pharmacology, genetics, genomics, agriculture.
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